1. Field of the Invention
The present invention is generally directed to image retrieval systems, and more particularly, to content-based retrieval of similar-looking images by employing multi-instance or relevance feedback to query images.
2. Description of the Related Art
The importance of content-based retrieval techniques for multimedia is assured in view of the widely used and distributed international coding standards, such as JPEG and MPEG, on the Internet. A particular multimedia content description interface, MPEG-7, is currently used to provide normal numerical descriptors for database search engine as matching criteria. For 2D shapes, MPEG-7 uses contour-based and region-based descriptors. Although describing shape contour by Fourier descriptors (FDs) can provide size, rotation and transition invariants for indexing, FDs are sensitive to noises and are more suitable for describing closed contour of shapes. For region-based descriptors, zernike and pseudo-zernike moments (ZMs and PZMs) are efficient features for retrieving similar shapes. Specific features such as edge orientation, aspect ratio, or complexity can be extracted for different databases and applications. Statistics, such as histogram or probability distribution model for the above-described features, are computed and considered as matching criteria for similarity measurement.
Shape descriptors have also been used to extract shape boundaries which are grouped into families on the basis of perceptual similarity. Visually salient feature is determined using probabilistic distribution model of trademarks in database and then trademarks with similar shape were retrieved according to this salient feature. A Multi-resolution description can be obtained by computing the mean and variance of each wavelet sub-band as to provide a concise description of the shape""s texture and shape. Shape features as discussed above can also be brought up according to specific databases and user requirements. Although efficient in retrieving similar shapes for one application, one set of universal descriptors cannot satisfy all specific requirements. Indeed, one set of descriptors may perform well for one database but not the other, and each user may need specific combination of features for their specific retrieval target.
Accordingly, an object of the present invention is to provide a content-based retrieval method and apparatus which retrieves statistically salient common features among sample query images having different feature sets.
It is another object of the present invention to update sample query images by multi-instance or relevance feedback.
It is still another object of the present invention to provide a universal query mechanism (UQM) or process that is plural and flexible in selecting proper features as to meet a user""s intent or requirement.
These and other objects of the present invention are achieved by providing a content-based retrieval method and apparatus which finds the most common features among each set of sample query images from multi-instance or relevance feedback. In particular, each set of sample query images is constructed by finding similar images shapes in the database. The resulting sample query images are statistically similar to query input, i.e. relative instead of absolute similarity. A probability distribution model for the feature vectors is used to dynamically adjust weights such that most common ones among sample query images dominates feedback query. Whenever new feature sets are devised, they could be acquired by the query system. The query unit then searches from all feature sets such that the statistically common features become the new query vector. Accordingly, the UQM accommodates new feature sets easily and adjusts weights for various features dynamically according to a user""s query and statistics of the database.
For similarity retrieval, each user has his definition for shape similarity and no one universal set of shape descriptors could satisfy all specific requirements. Usually, the indexing system provides relevance feedback to learn what user""s intention is and generating a new feature vector for next query. Instead of using mean of feature vectors (MFV) from sample query images as the new query vector, the UQM of the present invention finds statistically salient common features among sample query images with different feature sets.
Related aspects and advantages of the invention will become apparent and more readily appreciated from the following detailed description of the invention, taken in conjunction with the accompanying drawings.